Cloud cavitation on an oscillating hydrofoil

Cloud cavitation, often formed by the breakdown of a sheet or vortex cavity, is believed to be responsible for much of the noise and erosion damage that occurs under cavitating conditions. For this paper, cloud cavitation was produced through the periodic forcing of the flow by an oscillating hydrofoil. The present work examines the acoustic signal generated by the collapse of cloud cavitation, and compares the results to those obtained by studies of single travelling bubble cavitation. In addition, preliminary studies involving the use of air injection on the suction surface of the hydrofoil explore its mitigating effects on the cavitation noise

Capturing the Spectrum of Interaction Effects in Genetic Association Studies by Simulated Evaporative Cooling Network Analysis

Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF) and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC) feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN), whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs) and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important role for hubs in SNP disease susceptibility networks. The software is available at http://sites.google.com/site/McKinneyLab/software

Reducing dependence on big brother: Higher education looks for innovative funding opportunities

This paper presents innovative programs that business schools can utilize to reduce dependence on public funds. A review of the literature shows the theoretical and empirical foundation of higher education funding dilemmas. While higher education is moving towards a global ambition, scarcity hinders governments to fully support programs long-term; thus, cost-sharing and cost-shifting measures must occur for higher education to support current programs. In this study, we examine two universities (one U.S. and one UK.) and provide practical summaries of programs that have provided additional funds. We show that diversity of funding sources is essential for survival of higher education institutions. Market forces require competition to reduce higher education operational costs while providing students and corporate clients an a la carte educational experience

Inferring hidden Markov models from noisy time sequences: a method to alleviate degeneracy in molecular dynamics

We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al., and generates so called causal state models, equivalent to hidden Markov models. This method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods.Comment: 19 pages, 9 figure

Deep space payload launches via the Space Transportation System

The launching of deep space payloads via the Space Shuttle vehicle of the Space Transportation System, rather than via expendable launch vehicles, is described. Changes in procedures and data flow configurations for both the flight project and DSN during the launch period are required. A typical Galileo launch period sequence of events and telemetry and command data flow configurations are described

Cholinergic Modulation of Locomotion and Striatal Dopamine Release Is Mediated by α6α4* Nicotinic Acetylcholine Receptors

Dopamine (DA) release in striatum is governed by firing rates of midbrain DA neurons, striatal cholinergic tone, and nicotinic ACh receptors (nAChRs) on DA presynaptic terminals. DA neurons selectively express α6* nAChRs, which show high ACh and nicotine sensitivity. To help identify nAChR subtypes that control DA transmission, we studied transgenic mice expressing hypersensitive α6^(L9’S*) receptors. α6^(L9’S) mice are hyperactive, travel greater distance, exhibit increased ambulatory behaviors such as walking, turning, and rearing, and show decreased pausing, hanging, drinking, and grooming. These effects were mediated by α6 α4* pentamers, as α6^(L9’S) mice lacking α4 subunits displayed essentially normal behavior. In α6^(L9’S) mice, receptor numbers are normal, but loss of α4 subunits leads to fewer and less sensitive α6* receptors. Gain-of-function nicotine-stimulated DA release from striatal synaptosomes requires α4 subunits, implicating α6α4β2* nAChRs in α6^(L9’S) mouse behaviors. In brain slices, we applied electrochemical measurements to study control of DA release by α6^(L9’S) nAChRs. Burst stimulation of DA fibers elicited increased DA release relative to single action potentials selectively in α6^(L9’S), but not WT or α4KO/ α6^(L9’S), mice. Thus, increased nAChR activity, like decreased activity, leads to enhanced extracellular DA release during phasic firing. Bursts may directly enhance DA release from α6^(L9’S) presynaptic terminals, as there was no difference in striatal DA receptor numbers or DA transporter levels or function in vitro. These results implicate α6α4β2* nAChRs in cholinergic control of DA transmission, and strongly suggest that these receptors are candidate drug targets for disorders involving the DA system

Effects of Dehydration on Balance as Measured by the Balance Error Scoring System

The purpose of this study was to identify the effects of active dehydration on balance in euthermic individuals employing the Balance Error Scoring System (BESS). The results indicate that dehydration significantly negatively affects balance